Application research of convolution neural network in image classification of icing monitoring in power grid

Abstract

With the development of society, electricity has become an indispensable material, and the reliability of power grid has become more and more important. The ice-covered power grid will lead to accidents such as broken poles and other accidents, which seriously threaten the reliability of the power grid and safe operation. Therefore, a simple and efficient detection method of ice-covered power grid is urgently needed. To solve this problem, based on the good performance of convolution neural network, this paper applies it to the detection of power network icing. A classification method of power network icing detection image based on convolution neural network is proposed, which can effectively classify and recognize power network icing image. In addition, in view of the shortcomings of convolution neural network algorithm, this paper proposes a hybrid classification model combining convolution neural network and support vector machine. Firstly, the convolution neural network is used to extract features, and then the support vector machine is used to replace the softmax layer of the convolution neural network to realize the classification of ice-covered detection images. The simulation results show that it is feasible to use convolution neural network to classify the detection images of ice-covered power grid. Compared with convolution neural network, the hybrid classification model of convolution neural network and support vector machine proposed in this paper has better image classification effect, and further improves the classification performance of detection image of ice-covered power grid, and ensures the reliability and safe operation of power grid.